NEW Salford Predictive Modeler software suite. SPM Version 8.2 Just Released!
DOWNLOAD and try it for yourself today. See SPM 8.2 in action: SIGN UP FOR LIVE DEMO.
SPM 8.2
- Brainpower:
- 70+ pre-packaged automation scenarios inspired by the way leading model analysts structure their work.
- Efficiencies:
- Tools to relieve gruntwork, allowing the analyst to focus on the creative aspects of model development.
- Enhanced Algorithms:
- Regression, Classification, and Logistic Regression enhanced to support massive datasets.
- Improvements:
- New features for our core tools, based on user feedback and advances in data science.
- Bridging-the-gap
- between the leading edge academic thinking of Jerome Friedman and Leo Breiman and real-world applications.
Because Accuracy Matters
The SPM Salford Predictive Modeler® software suite is a highly accurate and ultra-fast platform for developing predictive, descriptive, and analytical models from databases and datasets of any size, complexity, or organization.
The SPM software suite’s data mining technologies span classification, regression, survival analysis, missing value analysis, data binning and clustering/segmentation to cover all aspects of your data science projects. SPM algorithms are considered to be essential in sophisticated data science circles
The SPM software suite‘s automation accelerates the process of model building by conducting substantial portions of the model exploration and refinement process for the analyst. While the analyst is always in full control we optionally anticipate the analysts’ next best steps and package a complete set of results from alternative modeling strategies for easy review.
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Automation
70+ pre-packaged scenarios, basically experiments, inspired by how leading model analysts structure their work. We call them "Automates". These "Automates" or experiments create multiple models automatically so that the analyst can easily see choices.
Example 1: Banking Applications
Automate Shaving
Automate Shaving helps to identify subsets of informative data within large datasets containing correlated variables within the account data. With automation, you may accomplish significant model reduction with minimal (if any) sacrifice to model accuracy. For example, start with a complete list of variables, and run automated shaving from the top to eliminate variables that look promising on the learn sample but fail to generalize. Later you can run shaving from the bottom to automatically eliminate a major bulk of redundant and unnecessary predictors. Then follow up with "shaving error" to quickly zero in on the most informative subset of features.
As opposed to typical data mining tools, Automate Shaving offers more than the typical variable importance list. Additionally, the analyst is provided with a full set of variable importance subsets/variations enabling the analyst to quickly optimize/select the final variable list and eliminating the burden of repetitive testing. Expert modelers typically devote a lot of time and effort to optimizing their variable importance list; Automate Shaving automates this process.
Example 2: Fraud Detection
Automate Priors
In typical fraud detection applications the analyst is concerned with identifying different sets of rules leading to a varying probability of fraud. Decision trees and TreeNet gradient boosting technology are typically used to build classification rules for detecting fraud. Any classification tree is constructed based on a specific user-supplied set of prior probabilities.
One set of priors will force trees to search for rules with high levels of fraud, while other sets of priors will produce trees with somewhat relaxed assumptions. To gain the most benefits of tree-based rule searching approaches, analysts will try a large number of different configurations of prior probabilities. This process is fully automated in Automate Priors. The result is a large collection of rules ranging from extremely high confidence fraud segments with low support to moderate indication of fraud segments with very wide support. For example, you can identify small segments with 100% fraud or you may find a large segment with a lesser probability of fraud, and everything in-between.
Example 3: Market Research - Surveys
Automate MVI (Missing Value Indicators)
In any survey, a large fraction of information may be missing. Often, the respondent will not answer questions either because they don't want to or are unable to do so. In addition to Salford Systems' expertise in handling missing values, a new automation feature allows the analyst to automatically generate multiple models including: 1) a model predicting response based solely on the pattern of missing values; 2) a model that automatically creates dummy missing value indicators in addition to the original set of predictors; and/or 3) a model that relies on engine-specific internal handling of missing values.
Example 4: Engineering Application
Automate Target
In a modern engineering application, as part of the experimental design, a large collection of sampled points may be gathered under different operating conditions. It can be challenging to identify mutual dependencies among the different parameters. For example, temperatures could be perfectly dependent on each other, or could be some unknown functions of other operating conditions like pressure and/or revolutions. Automate Target gives you powerful means to automatically explore and extract all mutual dependencies among predictors. By the word "dependencies," we mean a potentially nonlinear multivariate relationship that goes way beyond the simplicity of conventional correlations. Furthermore, as a powerful side effect, this Automate provides general means for missing value imputation, which is extremely useful to support those modeling engines that do not directly handle missing values.
Example 5: Web Advertising
Automate Sample
In an online ad placing application one has to balance the amount of data used vs. the time it takes to complete the model building. In web advertising there can be virtually an unlimited amount of data. So while ideally you would wish you use all available data, there is always a limit on how much can be used for real-time deployment. Automate Sample allows the analyst to automatically explore the impact of learn sample size on model accuracy. For example, you may discover that using 200,000,000 transactions provides no additional benefit in terms of model accuracy compared to 100,000,000 transactions.
Example 6: Microarray Application
Automate TARGETSHUFFLE
Microarray research datasets are characterized by an extremely large number of predictors (genes) and a very limited number of records (patients). This opens up a vast area of ambiguity resulting from the fact that even a random subset of predictors may produce a seemingly good looking model. Automate TARGETSHUFFLE allows you to determine whether the model performance is as accurate as it appears to be. Automate TARGETSHUFFLE automatically constructs a large number of auxiliary models based on randomly shuffled target variables. By comparing the actual model performance with the reference distribution (no dependency models), a final decision on model performance can be made. This technology could result in challenges to some of the currently produced papers in microarray research. If a dataset with deliberately destroyed target dependency can give you a model with good accuracy, then relying on the original model becomes rather dubious.
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Features List
SPM® 8.2 General Features.
| Components | Basic | Pro | ProEx | Ultra |
|---|---|---|---|---|
| Components | Basic | Pro | ProEx | Ultra |
| Modeling Engine: CART (Decision Trees) | o | o | o | o |
| Modeling Engine: MARS (Nonlinear Regression) | o | o | o | o |
| Modeling Engine: TreeNet (Stochastic Gradient Boosting) | o | o | o | o |
| Modeling Engine: RandomForests for Classification | o | o | o | o |
| Reporting ROC curves during model building and model scoring | o | o | o | o |
| Model performance stats based on Cross Validation | o | o | o | o |
| Model performance stats based on out of bag data during bootstrapping | o | o | o | o |
| Reporting performance summaries on learn and test data partitions | o | o | o | o |
| Reporting Gains and Lift Charts during model building and model scoring | o | o | o | o |
| Automatic creation of Command Logs | o | o | o | o |
| Built-in support to create, edit, and execute command files | o | o | o | o |
| Translating models into SAS-compatible language (all other languages supported for Pro and above) | o | o | o | o |
| Reading and writing datasets in all current database/statistical file formats, including csv file format | o | o | o | o |
| Option to save processed datasets into all current database/statistical file formats | o | o | o | o |
| Select Cases in Score Setup | o | o | o | o |
| TreeNet Scoring Offset in Score Setup | o | o | o | o |
| Setting of focus class supported for all categorical variables | o | o | o | o |
| Scalable limits on terminal nodes. This is a special mode that will ensure the ATOM and/or MINCHILD | o | o | o | o |
| Descriptive Statistics: Summary Stats, Stratified Stats, Charts and Histograms | o | o | o | |
| Activity Window: Brief data description, quick navigation to most common activities | o | o | o | |
| Additional Modeling Engines: Regularized Regression (LASSO/Ridge/LARS/Elastic Net/GPS) | o | o | o | |
| Data analysis Binning Engine | o | o | o | |
| Automatic creation of missing value indicators | o | o | o | |
| Option to treat missing value in a categorical predictor as a new level | o | o | o | |
| License to any level supported by RAM (currently 32MB to 1TB) | o | o | o | |
| License for multi-core capabilities | o | o | o | |
| Using built-in BASIC Programming Language during data preparation | o | o | o | |
| Automatic creation of lag variables based on user specifications during data preparation | o | o | o | |
| Automatic creation and reporting of key overall and stratified summary statistics for user supplied list of variables | o | o | o | |
| Display charts, histograms, and scatter plots for user selected variables | o | o | o | |
| Command Line GUI Assistant to simplify creating and editing command files | o | o | o | |
| Translating models into SAS/PMML/C/Java/Classic and ability to create classic and specialized reports for existing models | o | o | o | |
| Unsupervised Learning - Breiman's column scrambler | o | o | o | |
| Scoring any Automate (pre-packaged scenario of runs) as an ensemble model | o | o | o | |
| Summary statistics based on missing value imputation using scoring mechanism | o | o | o | |
| Impute options in Score Setup | o | o | o | |
| GUI support of SCORE PARTITIONS (GUI feature, SCORE PARTITIONS=YES) | o | o | o | |
| Quick Impute Analysis Engine: One-step statistical and model based imputation | o | o | o | |
| Advanced Imputation via Automate TARGET. Control over fill selection and new impute variable creation | o | o | o | |
| Correlation computation of over 10 different types of correlation | o | o | o | |
| Save OOB predictions from cross-validation models | o | o | o | |
| Custom selection of a new predictors list from an existing variable importance report | o | o | o | |
| User defined bins for Cross Validation | o | o | o | |
| Cross-Validation models can now be scored as an Ensemble | o | o | o | |
| An alternative to variable importance based on Leo Breiman's scrambler | o | o | o | |
| Data Binning Results display (GUI feature) | o | o | o | |
| Data Binning Analysis Engine bins variables using model-based binning (via AUTOMATE BIN), or using weights of evidence coding. | o | o | o | |
| BIN ROUND, ADAPTIVEROUND methods (BIN METHOD=ROUND/ADAPTIVEROUND) | o | o | o | |
| Controls for number of Bins and Deciles (BOPTIONS NBINS, NDECILES) | o | o | o | |
| EVAL command and GUI display (GUI feature) | o | o | o | |
| Summary stats for the correlations (Correlation Stats tab) (GUI feature) | o | o | o | |
| TONUMERIC: create contiguous integer variables from other variables | o | o | o | |
| Automation: Build two models reversing the roles of the learn and test samples (Automate FLIP) | o | o | o | |
| Automation: Explore model stability by repeated random drawing of the learn sample from the original dataset (Automate DRAW) | o | o | o | |
| Automation: For time series applications, build models based on sliding time window using a large array of user options (Automate DATASHIFT) | o | o | o | |
| Automation: Explore mutual multivariate dependencies among available predictors (Automate TARGET) | o | o | o | |
| Automated imputation of all missing values (via Automate Target) | o | o | o | |
| Automation: Explore the effects of the learn sample size on the model performance (Automate LEARN CURVE) | o | o | o | |
| Automation: Build a series of models by varying the random number seed (Automate SEED) | o | o | o | |
| Automation: Explore the marginal contribution of each predictor to the existing model (Automate LOVO) | o | o | o | |
| Automation: Explore model stability by repeated repartitioning of the data into learn, test, and possibly hold-out samples (Automate PARTITION) | o | o | o | |
| Automation: Explore the nonlinear univariate relationships between the target and each available predictor (Automate ONEOFF) | o | o | o | |
| Automation: Bootstrapping process (sampling with replacement from the learn sample) with a large array of user options (Random Forests-style sampling of predictors, saving in-bag and out-of-bag scores, proximity matrix, and node dummies) (Automate BOOTSTRAP) *not available in RandomForests | o | o | o | |
| Automation: AUTOMATE ENABLETIMING=YES|NO to control timing reporting in Automates | o | o | o | |
| Save out of bag predictions during Cross Validation | o | o | ||
| Use TREATMENT variables when scoring uplift models (SCORE EVAL) | o | o | ||
| Use TREATMENT variables when evaluating uplift model predictions (EVAL) | o | o | ||
| Automation: "Shifts" the "crossover point" between learn and test samples with each cycle of the Automate (Automate LTCROSSOVER) | o | o | ||
| Automation: Build a series of models using different backward variable selection strategies (Automate SHAVING) | o | o | ||
| Automation: Build a series of models using the forward-stepwise variable selection strategy (Automate STEPWISE) | o | o | ||
| Automation: Explore nonlinear univariate relationships between each available predictor and the target (Automate XONY) | o | o | ||
| Automation: Build a series of models using randomly sampled predictors (Automate KEEP) | o | o | ||
| Automation: Explore the impact of a potential replacement of a given predictor by another one (Automate SWAP) | o | o | ||
| Automation: Parametric bootstrap process (Automate PBOOT) | o | o | ||
| Automation: Build a series of models for each strata defined in the dataset (Automate STRATA) | o | o | ||
| Automation: Generate detailed univariate stats on every continuous predictor to spot potential outliers and problematic records (AUTOMATE OUTLIERS) | o | o | ||
| Automation: Convert (bin) all continuous variables into categorical (discrete) versions using a large array of user options (equal width, weights of evidence, Naïve Bayes, superwised) (AUTOMATE BIN) | o | o | ||
| Automation: Build a series of models using every available data mining engine (Automate MODELS) | o | |||
| Automation: Run TreeNet for Predictor selection, Auto-bin predictors, then build a series of models using every available data mining engine (Automate GLM) | o | |||
| Modeling Pipelines: RuleLearner, ISLE | o |
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University Program
Salford Systems' University Program provides SPM®, CART®, MARS®, TreeNet® , and RandomForests® at significantly-reduced licensing fees to the educational community. Eligible educational institutions are colleges, universities, community colleges, technical schools, and science centers. For more information on this special program, please contact our sales department.
Salford Systems is committed to supporting education and research in universities world-wide and offers special packaging and pricing.
We also offer academics cost-free access to our tutorial materials for classroom use.
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Webinars
The Evolution of Regression Modeling
The Evolution of Regression Modeling: from Classical Linear Regression to Modern Ensembles
Webinar Title: The Evolution of Regression Modeling: from Classical Linear Regression to Modern Ensembles
Date/Time: Friday, March 1, 15, 29, and April 12 2013, 10am-11am, PST
Course Description: Regression is one of the most popular modeling methods, but the classical approach has significant problems. This webinar series address these problems. Are you are working with larger datasets? Is your data challenging? Does your data include missing values, nonlinear relationships, local patterns and interactions? This webinar series is for you! We will cover improvements to conventional and logistic regression, and will include a discussion of classical, regularized, and nonlinear regression, as well as modern ensemble and data mining approaches. This series will be of value to any classically trained statistician or modeler.
Part 1
Part 1: Regression methods discussed (download slides)
- Classical Regression
- Logistic Regression
- Regularized Regression: GPS Generalized Path Seeker
- Nonlinear Regression: MARS Regression Splines
The Evolution of Regression Modeling
The Evolution of Regression Modeling: from Classical Linear Regression to Modern Ensembles
Part 2
Part 1:Step-by-step demonstration
- Datasets and software available for download
- Instructions for reproducing demo at your leisure
- For the dedicated student: apply these methods to your own data (optional)
The Evolution of Regression Modeling
The Evolution of Regression Modeling: from Classical Linear Regression to Modern Ensembles
Part 3
Part 3: Regression methods discussed (download slides)
*Part 1 is a recommended pre-requisite
- Nonlinear Ensemble Approaches: TreeNet Gradient Boosting; Random Forests; Gradient Boosting incorporating RF
- Ensemble Post-Processing: ISLE; RuleLearner
The Evolution of Regression Modeling
The Evolution of Regression Modeling: from Classical Linear Regression to Modern Ensembles
Part 4
Part 4: Hands-on demonstration of concepts discussed in part 3 (download slides)
- Step-by-step demonstration
- Datasets and software available for download
- Instructions for reproducing demo at your leisure
- For the dedicated student: apply these methods to your own data (optional)
The Evolution of Regression Modeling
The Evolution of Regression Modeling: from Classical Linear Regression to Modern Ensembles
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Advances in TreeNet Gradient Boosting
Advances in Gradient Boosting: The Power of Post Processing
Advances in Gradient Boosting: the Power of Post-Processing
Learn how TreeNet stochastic gradient boosting can be improved by post processing techniques such as GPS Generalized Path Seeker, RuleLearner, and ISLE.Course Outline:
I. Gradient Boosting and Post-Processing:
- What is missing from Gradient Boosting?
- Why post-processing techniques are used?
II. Applications Benefiting from Post-Processing: Examples from a variety of industries.
- Financial Services
- Biomedical
- Environmental
- Manufacturing
- Adserving
III. Typical Post-Processing Steps
IV. Techniques
- Generalized Path Seeker (GPS): Modern high-speed LASSO-style regularized regression
- Importance Sampled Learning Ensembles (ISLE): identify and reweight the most influential trees
- RuleLearner: ISLE on “steroids.” Identify the most influential nodes and rules
V. Case Study Example
- Output/Results without Post-Processing
- Output/Results with Post-Processing
- Demo
Watch the Video
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Combining CART and TreeNet
TreeNet Tree Ensembles and CART Decision Trees: A Winning Combination
TreeNet Tree Ensembles and CART Decision Trees: A Winning Combination
Combining CART decision trees with TreeNet stochastic gradient boosting: A winning combination.
Learn about how you can combine the best of both tools in this 1 hour webinar.
Course Outline
I. Classification and Regression Trees Pros/Cons
II. Stochastic Gradient Boosting: a promising way to overcome the shortcomings of a single tree
III. Introducing Stochastic Gradient Boosting, a powerful modern ensemble of boosted trees
- Methodology
- Reporting
- Interpretability
- Post-Processing
- Interaction Detection
IV. Advantages of using both Classification and Regression Trees and Tree Ensembles
Watch the Video
TreeNet Tree Ensembles and CART Decision Trees
TreeNet Tree Ensembles and CART Decision Trees: A Winning Combination
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Product Versions
SPM® 8.2 Product Versions
- Ultra
- The best of the best. For the modeler who must have access to leading edge technology available and fastest run times including major advances in ensemble modeling, interaction detection and automation. ULTRA also provides advance access to new features as they become available in frequent upgrades.
- ProEx
- For the modeler who needs cutting-edge data mining technology, including extensive automation of workflows typical for experienced data analysts and dozens of extensions to the Salford data mining engines.
- Pro
- A true predictive modeling workbench designed for the professional data miner. Variety of supporting conventional statistical modeling tools, programming language, reporting services, and a modest selection of workflow automation options.
- Basic
- Literally the basics. Salford Systems award winning data mining engines without extensions or automation or surrounding statistical services, programming language, and sophisticated reporting. Designed for small budgets while still delivering our world famous engines
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Requirements
-
We suggest the following minimum and recommended, system requirements:
- 80486 processor or higher.
- 512MB of random-access memory (RAM). This value depends on the "size" you have purchased (64MB, 128MB, 256MB, 512MB, 1GIG). While all versions may run with a minimum of 32MB of RAM, we CANNOT GUARANTEE they will. We highly recommend that you follow the recommended memory configuration that applies to the particular version you have purchased. Using less than the recommended memory configuration results in hard drive paging, reducing performance significantly, or application instability.
- Hard disk with 40 MB of free space for program files, data file access utility, and sample data files.
- Additional hard disk space for scratch files (with the required space contingent on the size of the input data set).
- CD-ROM or DVD drive.
Recommended System Requirements
Because Salford Tools are extremely CPU intensive, the faster your CPU, the faster they will run. For optimal performance, we strongly recommend they run on a machine with a system configuration equal to, or greater than, the following:
- Pentium 4 processor running 2.0+ GHz.
- 2 GIG of random-access memory (RAM). This value depends on the "size" you have purchased (64MB, 128MB, 256MB, 512MB, 1GIG). While all versions may run with a minimum of 32MB of RAM, we CANNOT GUARANTEE they will. We highly recommend that you follow the recommended memory configuration that applies to the particular version you have purchased. Using less than the recommended memory configuration results in hard drive paging, reducing performance significantly, or application instability.
- Hard disk with 40 MB of free space for program files, data file access utility, and sample data files.
- Additional hard disk space for scratch files (with the required space contingent on the size of the input data set).
- CD-ROM or DVD drive.
- 2 GIG of additional hard disk space available for virtual memory and temporary files.
Ensuring Proper Permissions
If you are installing on a machine that uses security permissions, please read the following note.
- You must belong to the Administrator group on Windows 2008, Windows 7 / 8 to be able to properly install and license. Once the application is installed and licensed, any member with read/write/modify permissions to the applications /bin and temp directories can execute and run the application.
-
Supported Architectures
- Alpha: DEC 3000 or AlphaServer running Tru64 UNIX 5.0 or higher
- Linux/i386: i586 or higher processor; Linux 2.4 or higher kernel; glibc 2.3 or higher
- Linux/AMD64: AMD64 or Intel EM64T processor; Linux 2.6 or higher kernel; glibc 2.3 or higher
- Sun: UltraSPARC processor; Solaris 2.6 or higher
- RS/6000: POWER or PowerPC processor; AIX 4.2 or higher
- HP 9000: PA/RISC 1.1 or higher processor; HP/UX 11.x
- SGI: MIPS 4 or higher processor; IRIX 6.5
Minimum System Requirements
- Minimum RAM requirement for all non-GUI app's is 32 MB of random-access memory (RAM). This value depends on the "size"
you have purchased (64MB, 128MB, 256MB, 512MB, 1GIG). - Hard disk with 40 MB of free space for program files, data file access utility, and sample data files.
- Additional hard disk space for scratch files (with the required space contingent on the size of the input data set).
Recommended System Requirements
- Recommended random-access memory (RAM) is 1.5 times the licensed data limit (32 MB, 64 MB, etc), up to the maximum permitted by the target architecture. On UNIX systems, it is generally recommended that there be at least twice as much swap space as there is RAM.
- Hard disk with 40 MB of free space for program files, data file access utility, and sample data files.
- Additional hard disk space for scratch files (with the required space contingent on the size of the input data set).
All Salford apps are very CPU intensive, so more memory and a faster CPU are always helpful.
Licensing Application
The Salford Predictive Modeler uses a system of application system ID and associated unlock key. When installation is complete, the user will need to email the application "system ID." This system ID is clearly displayed in the License Information displayed the first time the application is started. You can alternatively get to this window by selecting the Help->License menu option.
Method 1: Fixed License
With a fixed license, each machine must have its own copy of the licensed program installed. If your license terms permit more than one copy, then the license must be activated on each machine that will be used.
Method 2: Floating License
This method of licensing your program is used if you intend the program application to be used by more than one user concurrently over a network. A floating license tracks the number of copies "checked out." When that number exceeds your license terms, a message is provided informing the user "all copies are checked out." The licensed program may be installed on a machine that each client machine can access. Machines that are not connected to the network must be issued a fixed license (Method 1 above).
A floating license is particularly useful when the number of potential users exceeds the number of seats specified in your license terms.
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Scalability
A user's license sets a limit on the amount of learn sample data that can be analyzed. The learn sample is the data used to build the model. Note that there is no limit to the number of test sample data points that may be analyzed. In other words, rows -by- columns of variables and observations used to build the model. Variable not used in the model do not count. Observations reserved for testing, or excluded for other reasons, do not count.
For example, suppose our 32MB version that sets a learn sample limitation of 8 MB. Each data point occupies 4 bytes. For instance, a 8MB capacity license will allow up to 8 * 1024 * 1024 / 4 = 2,097,152 learn sample data points to be analyzed.A data point is a represented by a 1-variable by- 1-observation (1-row by-1-column).
The following is a table that describes the current set of "sizes" available. Please note that the minimum required RAM is **not** the same as the learn sample limitation.
| Size | Data Limit MB | Data Limit # of values | |
|
minimum required
physical memory
(RAM) in MB
|
Licensed learn sample
data sizein MB
(1 MB = 1,048,576 bytes)
|
Licensed # of learn
sample values
(rows by columns)
|
|
| 32 | 8 | 2,097,152 | |
| 64 | 18 | 4,718,592 | |
| 128 | 45 | 11,796,480 | |
| 256 | 100 | 26,214,400 | |
| 512 | 200 | 52,428,800 | |
| 1024 | 400 | 104,857,600 | |
| 2048 | 800 | 209,715,200 | **64-bit only |
| 3072 | 1200 | 324,572,800 | **64-bit only |
Additional larger capacity is available under 64-bit operating systems, using our non-GUI (command-line) builds. The non-GUI is very flexible and can be licensed for large data limits not currently available in the GUI product line. The current MAXIMUM is 8-GIG data capacity for our non-GUI builds.
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Tags: v8, Salford Predictive Modeler, SPM, Salford-Systems